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Docs Machine Learning 101

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Artificial Intelligence vs Machine Learning vs Deep Learning

Knowledge needed to master in this area

Mathematics (http://www.deeplearningbook.org/)

Type of Machine Learning Algorithms

alt text

TERMINOLOGY

Arthur Samuel: Field of study that gives computers the ability to learn without being explicitly programmed. Tom Mitchell: A computer program is said to learn from experience E with respect to some task T and some performance measure P, if its performance on T, as measured by P, improves with experience E.

List of Machine Learning Algorithms (Examples)

Linear Regression

Finding straight line or hyperplane that best fits to set of points

Multi Feature Variables in Linear Regression

Scaling and Mean Normalization

Alt Text

Comparison Gradient Descent and Normal Equation

Reducing loss

As we train a model reducing loss will give best trained model. Stochastic Gradient Descent involves one example at a time while for Mini-Batch Gradient Descent batches 10-1000

Type of Training models

What is Regression Analysis?

Regression Analysis investigates the relationship between predictor (independent variable) and target (dependent variable) where it is predictive modelling technique. Examples including time series, forecasting as well as finding casual effects relationship between variables.

Linear Regression

To estimate real values based on continous variables (using regression line represents by linear equation y=ax+b; where y=dependent variable, a=slope, x=independent variable, b=intercept)

Type of Linear Regression

Python code for Linear Regression

#Import Library
#Import other necessary libraries like pandas, numpy...
from sklearn import linear_model
#Load Train and Test datasets
#Identify feature and response variable(s) and values must be numeric and numpy arrays
x_train=input_variables_values_training_datasets
y_train=target_variables_values_training_datasets
x_test=input_variables_values_test_datasets
# Create linear regression object
linear = linear_model.LinearRegression()
# Train the model using the training sets and check score
linear.fit(x_train, y_train)
linear.score(x_train, y_train)
#Equation coefficient and Intercept
print('Coefficient: \n', linear.coef_)
print('Intercept: \n', linear.intercept_)
#Predict Output
predicted= linear.predict(x_test)

R code for Linear Regression

#Load Train and Test datasets
#Identify feature and response variable(s) and values must be numeric and numpy arrays
x_train <- input_variables_values_training_datasets
y_train <- target_variables_values_training_datasets
x_test <- input_variables_values_test_datasets
x <- cbind(x_train,y_train)
# Train the model using the training sets and check score
linear <- lm(y_train ~ ., data = x)
summary(linear)
#Predict Output
predicted= predict(linear,x_test)

Other type of Regressions

Logistic Regression

Decision Tree

SVM

Naive Bayes

kNN

K-Means

Random Forest

Dimension Reduction Algorithm

Gradient Boosting Algorithms:

K-Means clustering

K-Mean clustering use a method of vector quantization for (cluster analysis). Cluster analysis is where grouping task is among similarity to each other for same group. It is an unsupervised learning. alt text

Steps in K-Means

Determine the best value for K

Using Elbow method, whithin a range of values of K, example (1 to 10) then calculate the sum of squared errors. alt text Calculation for sum of square errors alt text

CREDIT/REFERENCES

Blog

r-bloggers-K-Means

Online Course

Machine Learning Crash Course

Website

Common-machine-learning-algorithms

Github

Siraj Raval

Big Data

Three major sources of big data

Characteristics of Big Data

Big Data Engineering

Hadoop Ecosystem

Big Data Ecosystem

Hadoop Command Lines

Hadoop Components

Data Lakes

Data models

Information Security

Progress Status

Android App Security

Android provides a sandboxed app execute env. A customized embedded Linux system interacts with the phone hardware and an off-processor cellular radio.

Java compiler creates JVM bytecode, the Dalvik dx compiler consumes the .class files, recompiles them to Dalvik bytecode and writes the app into single.dex file.

The process consists of translation, reconstruction and interpretation of 3 basic elements of the app
the constant pools, class definitions and data segment.

Constant pool: constant (references to other classes,method names, numerical constant) Class definitions: basic infos(access flags, class names) Data segment: method code executed by target VM number of DVM registers used, local variable table, and operand stack sizes), class and instance variable

Risks

Aims

Hypothesis

Computer science terms

Differences of JVM and DVM

Ded decompiler

Development of Android Apk

Prerequisites

Wireless Commands

Android hacking

Security Updates

Nmap and Wireshark for Network Scanning

Saving result

Windows CMD /Netstat/ ping /tracert/ ARP

Creating Windows Payload with Msfvenom

AlienVault/ DVWA/ OSForensics/ ProDiscover Basic

References

Terminologies

Penetration Test

Set of methods and procedures for testing or protecing security of an organization.

Vulnerability Assessments

To check for vulnerabilities and document accordingly.

Rules of Engagement

Phases in Penetration Testing (With tools)

Security Analyst Tracks

Auditing and Incident Response

Security Threat

Categories

Mitigate escalation of privilege threats

Mitigate brute force attacks

Slowloris attack (Dos attack //An attack that renders systems unresponsive)

Causes lead to security misconfiguration threats

# Spoofing

# Integrity and Tampering Threats

# Authentication and Non-repudiation

# Information Privacy and Confidentiality

# Nmap

# Network IDS

# Host IDS

# IDS with Snort

# IDS with Bro

# Evading IDS with Nmap //-n skipping DNS resolution

# Brute force analysis

# Nmap script with its test

# User account discovery

# Firewall

# VPN

# Penetration Testing

# Type of PenTest

# Buffer Overflow Exploits ## Prevention

Privilege Escalation Attacks

Client- side Attacks

Incident Response Stages

  1. Preparation
  2. Identification
  3. Containment
  4. Eradication
  5. Recovery
  6. Lessons Learned

Tools in Incident Respone

OODA Loop (Observe, Orient, Decode, Act)

Malware

Antimalware

Security awareness

Digital forensics hardwares

Digital forensics softwares

Android tools

iOS Tools

Reference: https://www.practice-labs.com/

Nmap

Hping

Nslookup

Setup Honeypot

Penetration Testing

Cyber attack

Scanning tools

Credential testing tools

Contact Types

Resources

Rules of engagement

Compliance-based Assessments

Google Hacking

Harvester

Recon-ng

Wireless

EFS

Bitlocker

fdisk -l

apt-get install cryptsetup cryptsetup -y -v luksFormat /dev/sdb cryptsetup -v status mkfs.ext4 unmount cryptsetup luksClose cyrptsetup luksOpen

Hashing

SSL

Configuration Management Process

Server side attack

client side attack

Security Principle

Incident Response

Access control concept

Networking

Ports

Type of threats

Network design

Data security

Cloud computing